Evaluation of Regional Aircraft Observations Using TAMDAR

Abstract

A multiyear evaluation of a regional aircraft observation system [Tropospheric Aircraft Meteorological Data Reports (TAMDAR)] is presented. TAMDAR observation errors are compared with errors in traditional reports from commercial aircraft [aircraft meteorological data reports (AMDAR)], and the impacts of TAMDAR observations on forecasts from the Rapid Update Cycle (RUC) over a 3-yr period are evaluated. Because of the high vertical resolution of TAMDAR observations near the surface, a novel verification system has been developed and employed that compares RUC forecasts against raobs every 10 hPa; this revealed TAMDAR-related positive impacts on RUC forecasts-particularly for relative humidity forecasts-that were not evident when only raob mandatory levels were considered. In addition, multiple retrospective experiments were performed over two 10-day periods, one in winter and one in summer; these allowed for the assessment of the impacts of various data assimilation strategies and varying data resolutions. TAMDAR's impacts on 3-h RUC forecasts of temperature, relative humidity, and wind are found to be positive and, for temperature and relative humidity, substantial in the region, altitude, and time range over which TAMDARequipped aircraft operated during the studied period of analysis.

Evaluation of the Impacts of Assimilating the TAMDAR Data on 12/4 km Grid WRF-Based RTFDDA Simulations over the CONUS

“…Compared to the previous papers that study the impacts of the TAMDAR data on the model simulations (e.g., [14,21,22]), the findings in this work confirm that within the 400-600 hPa layer assimilating the TAMDAR data consistently improves RMSE and correlation for temperature and humidity for both domains and wind speed for Domain 1. Nevertheless, negative impacts for wind speed are identified for this layer for high-resolution Domain 2 and Domain 1 generally shows relatively larger positive impacts of assimilating the TAMDAR data than Domain 2.…”

“…Preliminary studies using the TAMDAR data in a real-time four-dimensional data assimilation (RTFDDA) and forecasting system showed positive impacts of the TAMDAR data on mesoscale analyses and simulations [14,[17][18][19][20]. Subsequent analyses of the TAM-DAR data impacts using different data assimilation systems have also shown encouraging results on NWP forecasts over the CONUS [21,22] and on hurricane track predictions [23]. Building on the works of Liu et al [17,18] and Jacobs et al [14], the National Center for Atmospheric Research (NCAR) and AirDat LLC started in July 2009 a collaboration that led to the development of the operational NWP forecasts for the CONUS domain at 12 km and 4 km resolution (Figure 1) using the Weather Research and Forecasting-(WRF-) based RTFDDA analysis and forecasting system [24,25].…”

Implementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

“…Hence, while the basic aim of the paper is to show the general characteristics of the 3D-Var, an application to the data assimilation of tropospheric profiles is also given. Indeed, the numerical experiment set-up of this paper should be of interest to the atmospheric profiling community because it can be used in OSE (observing system experiment), which allows for the objective assessment and comparison of existing observing systems, or in OSSE (observing system simulation experiment), whose aim is to show the impact of next generation observing systems in a controlled software environment such as weather prediction models (Otkin et al, 2011;Moninger et al, 2010).…”

TAMDAR Observation Assimilation in WRF 3D-Var and Its Impact on Hurricane Ike (2008) Forecast

“…The observation errors of wind speed, direction, and temperature for TAMDAR are 3.6 m s −1 , 10 degree, and 1 K, respectively, which are the same as those of the AIREP in WRF 3D-Var. The observation error of relative humidity is set to 10%, which is comparable to the optimal error 12 % used in the study by Moninger et al (2007).…”

“…Recent works have shown promising impacts of TAMDAR data on severe weather forecasts over the continental United States (e.g., Jacobs et al, 2006;Liu et al, 2007;Moninger et al, 2007Moninger et al, , 2010. These works were conducted in the contexts of the Rapid Update Cycle (RUC) data assimilation and forecast system (Benjamin et al, 2004) or a four-dimensional nudging data assimilation system (Liu et al, 2007).…”